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HEp-2 Staining Pattern Classification

Strandmark, Petter LU ; Ulén, Johannes LU and Kahl, Fredrik LU (2012) 21st International Conference on Pattern Recognition (ICPR 2012) In Pattern Recognition (ICPR), 2012 21st International Conference on
Abstract
Classifying images of HEp-2 cells from indirect immunofluorescence has important clinical applications. We have developed an automatic method based on random forests that classifies an HEp-2 cell image into one of six classes. The method is applied to the data set of the ICPR 2012 contest. The previously obtained best accuracy is 79.3% for this data set, whereas we obtain an accuracy of 97.4%. The key to our result is due to carefully designed feature descriptors for multiple level sets of the image intensity. These features characterize both the appearance and the shape of the cell image in a robust manner.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Pattern Recognition (ICPR), 2012 21st International Conference on
pages
4 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
21st International Conference on Pattern Recognition (ICPR 2012)
external identifiers
  • scopus:84874579172
ISBN
978-1-4673-2216-4
language
English
LU publication?
yes
id
15a4fd9f-eadc-4dd6-9512-05dd59efc9fa (old id 3437296)
alternative location
http://www.maths.lth.se/vision/publdb/reports/pdf/strandmark-ulen-etal-icpr.pdf
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460065
date added to LUP
2013-09-06 18:13:27
date last changed
2017-04-09 04:38:33
@inproceedings{15a4fd9f-eadc-4dd6-9512-05dd59efc9fa,
  abstract     = {Classifying images of HEp-2 cells from indirect immunofluorescence has important clinical applications. We have developed an automatic method based on random forests that classifies an HEp-2 cell image into one of six classes. The method is applied to the data set of the ICPR 2012 contest. The previously obtained best accuracy is 79.3% for this data set, whereas we obtain an accuracy of 97.4%. The key to our result is due to carefully designed feature descriptors for multiple level sets of the image intensity. These features characterize both the appearance and the shape of the cell image in a robust manner.},
  author       = {Strandmark, Petter and Ulén, Johannes and Kahl, Fredrik},
  booktitle    = {Pattern Recognition (ICPR), 2012 21st International Conference on},
  isbn         = {978-1-4673-2216-4},
  language     = {eng},
  pages        = {4},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {HEp-2 Staining Pattern Classification},
  year         = {2012},
}